Sentiment Analysis: an Application to Anadolu University

Öztürk, Zehra Kamışlı; Cicek, Zeynep Ídil Erzurum; Ergül, Zehra Güzel

doi:10.12693/aphyspola.132.753

Cited by 26 publications

(14 citation statements)

References 16 publications

(16 reference statements)

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“…Assessment and evaluation studies also largely focused on the level of teaching and learning (86%, n = 31), although five studies described applications at the institutional level. In order to gain an overview of student opinion about online and distance learning at their institution, academics at Anadolu University (Ozturk, Cicek, & Ergul, 2017) used sentiment analysis to analyse mentions by students on Twitter, using Twitter API Twython and terms relating to the system. This analysis of publicly accessible data, allowed researchers insight into student opinion, which otherwise may not have been accessible through their institutional LMS, and which can inform improvements to the system.…”

Section: Assessment and Evaluationmentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,296

468

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Section: Assessment and Evaluationmentioning

confidence: 99%

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Zawacki‐Richter

Marín

Bond

et al. 2019

Int J Educ Technol High Educ

1,296

468

View full text Add to dashboard Cite

show abstract

“…Nevertheless, as far as we know, the only SA study, which is about an open and distance education system in Turkish, is given by our sentiment research group (Kamisli Ozturk et al, 2017). Our previous study applied SA for a smaller dataset by using just one classifier as Naive Bayes.…”

Section: Social Mediamentioning

confidence: 99%

Turkish Sentiment Analysis for Open and Distance Education Systems

Aydin¹,

Öztürk²,

Cicek³

2021

Turkish Online Journal of Distance Education

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Students' opinions are the most essential source to enhance the quality of education and educational services in Open and Distance education (ODE) Systems. How to access and analyze students' real opinions is a problem for ODE institutions. The purpose of the present study is to conduct a sentiment analysis (SA) on the collected Turkish tweets about an ODE system to monitor students' opinions and sentiments about the system. Firstly, the related 63699 tweets about the ODE system are gathered and analyzed. Later, preprocessing is applied to the dataset. Sentence-based SA is performed with the data provided. The dataset is vectorized using two vector space models to test four classifiers which are Support Vector Machines, K-Nearest Neighbor, Logistic Regression (LR), and Artificial Neural Networks. F-score values obtained with these classifiers are evaluated, and the results are discussed. LR classifier gives the best F-score values with %75 for each vector space model. Through the SA results, students' dissatisfaction, appreciation, and concerns will be learned quickly by the university administration to develop strategies that will increase the quality of education and educational services.

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“…Tweet texts are usually lacking a formal writing standard and because of that each text is purified by implementing the steps in Table 1 to create a sounder model [ 30 , 31 ]. Purpose of the data preprocessing is to achieve more sensible results by decreasing the size of feature [ 32 – 34 ].…”

Section: Proposed Systemmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

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As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

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Sentiment Analysis: an Application to Anadolu University

Cited by 26 publications

References 16 publications

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Turkish Sentiment Analysis for Open and Distance Education Systems

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

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